TERADATA CLOUDANALYTICS MOVES BUSINESS FORWARD.

Teradata Vantage delivers a powerful, modern analytics cloud architecture that unifies Analytics, Data Lakes, and Data Warehouses. For those already in the cloud, Teradata Vantage can flexibly scale to meet the challenge of growth – and pay-as-you-go offers maximum flexibility. Whether an organization is migrating to the cloud, has a hybrid infrastructure, or is already fully committed, Teradata moves from cloud analytics to answers.

Why Cloud?

It’s a fact: in an enterprise-level business, the cloud isn’t optional—the cloud IS business. Information technology is being reshaped to take advantage of new standards of speed, storage, and limitless scale. With the cloud offering greater flexibility, massive (and low-cost) storage, more expansive service capabilities, it’s easy to understand the cloud’s rapid growth.

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Recommended First Use Cases

More and more analytic ecosystems are making their way into the cloud. Scenarios where the elasticity provided by cloud deployment is quite advantageous include test and development systems as well as data labs. These use cases share the characteristic of being needed for a short time and then no longer needed for awhile.

Cloud for Test and Development

In many organizations, the capacity of test and development systems is often under-utilized - but when activity picks up, capacity can be under-sized. This paradox can be addressed with cloud deployment. The flexibility to allocate exactly the resources needed for testing and/or development, then turn around and de-allocate just as quickly is very powerful. An added advantage for test and development is the ability to use the latest versions of software for feature exploitation and/or validation before the software is ready to be deployed in a highly governed on-premises environment.

Cloud for Data Labs

In data lab deployment, cloud implementation has two big advantages. First, scalability in cloud provides the perfect working model for users who cannot necessarily anticipate resources needed for working with the next new data set or algorithm. The second advantage of cloud deployment for data science work is agility. Internal process and timeframes for bringing in a new piece of software on-premises can slow down data discovery. In a cloud data lab environment, the desired tools are more easily accessible, even without going through a maze of IT governance and standards committees.